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Neural Networks Training Courses

Local, instructor-led live Neural Network training courses demonstrate through interactive discussion and hands-on practice how to construct Neural Networks using a number of mostly open-source toolkits and libraries as well as how to utilize the power of advanced hardware (GPUs) and optimization techniques involving distributed computing and big data. Our Neural Network courses are based on popular programming languages such as Python, Java, R language, and powerful libraries, including TensorFlow, Torch, Caffe, Theano and more. Our Neural Network courses cover both theory and implementation using a number of neural network implementations such as Deep Neural Networks (DNN), Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN).

Neural Network training is available as "onsite live training" or "remote live training". Denmark onsite live Neural Networks trainings can be carried out locally on customer premises or in NobleProg corporate training centers. Remote live training is carried out by way of an interactive, remote desktop.

NobleProg -- Your Local Training Provider

Testimonials

★★★★★

★★★★★

Ref material to use later was very good.

PAUL BEALES- Seagate Technology.

Course: Applied Machine Learning

What did you like the most about the training?:
Gave me good practice with using R to build machine learning systems for real situations. I can use this in my work straight away.
This was an excellent course. One of the best I have had.

Matthew Thomas - British Telecom

Course: Applied Machine Learning

It was very interactive and more relaxed and informal than expected. We covered lots of topics in the time and the trainer was always receptive to talking more in detail or more generally about the topics and how they were related. I feel the training has given me the tools to continue learning as opposed to it being a one off session where learning stops once you've finished which is very important given the scale and complexity of the topic.

Jonathan Blease

Course: Artificial Neural Networks, Machine Learning, Deep Thinking

Ann created a great environment to ask questions and learn. We had a lot of fun and also learned a lot at the same time.

Gudrun Bickelq

Course: Introduction to the use of neural networks

The interactive part, tailored to our specific needs.

Thomas Stocker

Course: Introduction to the use of neural networks

I really appreciated the crystal clear answers of Chris to our questions.

Léo Dubus

Course: Neural Networks Fundamentals using TensorFlow as Example

I generally enjoyed the knowledgeable trainer.

Sridhar Voorakkara

Course: Neural Networks Fundamentals using TensorFlow as Example

I was amazed at the standard of this class - I would say that it was university standard.

David Relihan

Course: Neural Networks Fundamentals using TensorFlow as Example

Very good all round overview. Good background into why Tensorflow operates as it does.

Kieran Conboy

Course: Neural Networks Fundamentals using TensorFlow as Example

I liked the opportunities to ask questions and get more in depth explanations of the theory.

Sharon Ruane

Course: Neural Networks Fundamentals using TensorFlow as Example

I liked the new insights in deep machine learning.

Josip Arneric

Course: Neural Network in R

We gained some knowledge about NN in general, and what was the most interesting for me were the new types of NN that are popular nowadays.

Neural Networks Subcategories

Neural Networks Course Outlines

This course covers AI (emphasizing Machine Learning and Deep Learning) in Automotive Industry. It helps to determine which technology can be (potentially) used in multiple situation in a car: from simple automation, image recognition to autonomous decision making.

The Tensor Processing Unit (TPU) is the architecture which Google has used internally for several years, and is just now becoming available for use by the general public. It includes several optimizations specifically for use in neural networks, including streamlined matrix multiplication, and 8-bit integers instead of 16-bit in order to return appropriate levels of precision。

In this instructor-led, live training, participants will learn how to take advantage of the innovations in TPU processors to maximize the performance of their own AI applications.

By the end of the training, participants will be able to:

- Train various types of neural networks on large amounts of data.- Use TPUs to speed up the inference process by up to two orders of magnitude.- Utilize TPUs to process intensive applications such as image search, cloud vision and photos.

Format of the course

- Part lecture, part discussion, exercises and heavy hands-on practice

Snorkel is a system for rapidly creating, modeling, and managing training data. It focuses on accelerating the development of structured or "dark" data extraction applications for domains in which large labeled training sets are not available or easy to obtain.

In this instructor-led, live training, participants will learn techniques for extracting value from unstructured data such as text, tables, figures, and images through modeling of training data with Snorkel.

Microsoft Cognitive Toolkit 2.x (previously CNTK) is an open-source, commercial-grade toolkit that trains deep learning algorithms to learn like the human brain. According to Microsoft, CNTK can be 5-10x faster than TensorFlow on recurrent networks, and 2 to 3 times faster than TensorFlow for image-related tasks.

In this instructor-led, live training, participants will learn how to use Microsoft Cognitive Toolkit to create, train and evaluate deep learning algorithms for use in commercial-grade AI applications involving multiple types of data such as data, speech, text, and images.

By the end of this training, participants will be able to:

- Access CNTK as a library from within a Python, C#, or C++ program- Use CNTK as a standalone machine learning tool through its own model description language (BrainScript)- Use the CNTK model evaluation functionality from a Java program- Combine feed-forward DNNs, convolutional nets (CNNs), and recurrent networks (RNNs/LSTMs)- Scale computation capacity on CPUs, GPUs and multiple machines- Access massive datasets using existing programming languages and algorithms

Audience

- Developers- Data scientists

Format of the course

- Part lecture, part discussion, exercises and heavy hands-on practice

Note

- If you wish to customize any part of this training, including the programming language of choice, please contact us to arrange.

Artificial intelligence has revolutionized a large number of economic sectors (industry, medicine, communication, etc.) after having upset many scientific fields. Nevertheless, his presentation in the major media is often a fantasy, far removed from what really are the fields of Machine Learning or Deep Learning. The aim of this course is to provide engineers who already have a master's degree in computer tools (including a software programming base) an introduction to Deep Learning as well as to its various fields of specialization and therefore to the main existing network architectures today. If the mathematical bases are recalled during the course, a level of mathematics of type BAC + 2 is recommended for more comfort. It is absolutely possible to ignore the mathematical axis in order to maintain only a "system" vision, but this approach will greatly limit your understanding of the subject.

This course has been created for managers, solutions architects, innovation officers, CTOs, software architects and anyone who is interested in an overview of applied artificial intelligence and the nearest forecast for its development.

In this instructor-led, live training, participants will learn how to create various neural network components using ENCOG. Real-world case studies will be discussed and machine language based solutions to these problems will be explored.

Deep Reinforcement Learning refers to the ability of an "artificial agent" to learn by trial-and-error and rewards-and-punishments. An artificial agent aims to emulate a human's ability to obtain and construct knowledge on its own, directly from raw inputs such as vision. To realize reinforcement learning, deep learning and neural networks are used. Reinforcement learning is different from machine learning and does not rely on supervised and unsupervised learning approaches.

In this instructor-led, live training, participants will learn the fundamentals of Deep Reinforcement Learning as they step through the creation of a Deep Learning Agent.

This instructor-led, live course provides an introduction into the field of pattern recognition and machine learning. It touches on practical applications in statistics, computer science, signal processing, computer vision, data mining, and bioinformatics.

The course is interactive and includes plenty of hands-on exercises, instructor feedback, and testing of knowledge and skills acquired.

Artificial Neural Network is a computational data model used in the development of Artificial Intelligence (AI) systems capable of performing "intelligent" tasks. Neural Networks are commonly used in Machine Learning (ML) applications, which are themselves one implementation of AI. Deep Learning is a subset of ML.

This training course is for people that would like to apply Machine Learning in practical applications.

Audience

This course is for data scientists and statisticians that have some familiarity with statistics and know how to program R (or Python or other chosen language). The emphasis of this course is on the practical aspects of data/model preparation, execution, post hoc analysis and visualization.

The purpose is to give practical applications to Machine Learning to participants interested in applying the methods at work.

Sector specific examples are used to make the training relevant to the audience.

Artificial Neural Network is a computational data model used in the development of Artificial Intelligence (AI) systems capable of performing "intelligent" tasks. Neural Networks are commonly used in Machine Learning (ML) applications, which are themselves one implementation of AI. Deep Learning is a subset of ML.

Part-3(40%) of the training would be extensively based on Tensorflow - 2nd Generation API of Google's open source software library for Deep Learning. The examples and handson would all be made in TensorFlow.

Audience

This course is intended for engineers seeking to use TensorFlow for their Deep Learning projects

After completing this course, delegates will:

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have a good understanding on deep neural networks(DNN), CNN and RNN

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understand TensorFlow’s structure and deployment mechanisms

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be able to carry out installation / production environment / architecture tasks and configuration

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be able to assess code quality, perform debugging, monitoring

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be able to implement advanced production like training models, building graphs and logging